Empirical comparison between autoencoders and traditional dimensionality reduction methods

• URL: http://arxiv.org/abs/2103.04874v1
• Date: Mon, 8 Mar 2021 16:26:43 GMT
• Title: Empirical comparison between autoencoders and traditional dimensionality reduction methods
• Authors: Quentin Fournier and Daniel Aloise
• Abstract summary: We evaluate the performance of PCA compared to Isomap, a deep autoencoder, and a variational autoencoder. Experiments revealed that k-NN achieved comparable accuracy on PCA and both autoencoders' projections provided a big enough dimension.
• Score: 1.9290392443571387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In order to process efficiently ever-higher dimensional data such as images, sentences, or audio recordings, one needs to find a proper way to reduce the dimensionality of such data. In this regard, SVD-based methods including PCA and Isomap have been extensively used. Recently, a neural network alternative called autoencoder has been proposed and is often preferred for its higher flexibility. This work aims to show that PCA is still a relevant technique for dimensionality reduction in the context of classification. To this purpose, we evaluated the performance of PCA compared to Isomap, a deep autoencoder, and a variational autoencoder. Experiments were conducted on three commonly used image datasets: MNIST, Fashion-MNIST, and CIFAR-10. The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space. Then a k-NN classifier was trained on each projection with a cross-validated random search over the number of neighbours. Interestingly, our experiments revealed that k-NN achieved comparable accuracy on PCA and both autoencoders' projections provided a big enough dimension. However, PCA computation time was two orders of magnitude faster than its neural network counterparts.

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